README.md

Awesome Adversarial Machine Learning:

A curated list of awesome adversarial machine learning resources, inspired by awesome-computer-vision.

Table of Contents

• Blogs
• Papers
• Talks

Blogs

• Breaking Linear Classifiers on ImageNet, A. Karpathy et al.
• Breaking things is easy, N. Papernot & I. Goodfellow et al.
• Attacking Machine Learning with Adversarial Examples, N. Papernot, I. Goodfellow, S. Huang, Y. Duan, P. Abbeel, J. Clark.
• Introduction to Adversarial Machine Learning, Sarah Jamie Lewis.

Papers

General

• Intriguing properties of neural networks, C. Szegedy et al., arxiv 2014
• Explaining and Harnessing Adversarial Examples, I. Goodfellow et al., ICLR 2015

Attack

Image Classification

• DeepFool: a simple and accurate method to fool deep neural networks, S. Moosavi-Dezfooli et al., CVPR 2016
• The Limitations of Deep Learning in Adversarial Settings, N. Papernot et al., ESSP 2016
• Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples, N. Papernot et al., arxiv 2016
• Adversarial Examples In The Physical World, A. Kurakin et al., ICLR workshop 2017
• Delving into Transferable Adversarial Examples and Black-box Attacks Liu et al., ICLR 2017
• Towards Evaluating the Robustness of Neural Networks N. Carlini et al., SSP 2017
• Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples, N. Papernot et al., Asia CCS 2017

Reinforcement Learning

• Adversarial attacks on neural network policies, S. Huang et al, ICLR workshop 2017
• Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents, Y. Lin et al, IJCAI 2017
• Delving into adversarial attacks on deep policies, J. Kos et al., ICLR workshop 2017

VAE-GAN

• Adversarial examples for generative models, J. Kos et al. arxiv 2017

Defence

Adversarial Training

• Adversarial Machine Learning At Scale, A. Kurakin et al., ICLR 2017
• Ensemble Adversarial Training: Attacks and Defenses, F. Tramèr et al., arxiv 2017

Defensive Distillation

• Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks, N. Papernot et al., SSP 2016
• Extending Defensive Distillation, N. Papernot et al., arxiv 2017

Regularization

• Distributional Smoothing with Virtual Adversarial Training, T. Miyato et al., ICLR 2016
• Adversarial Training Methods for Semi-Supervised Text Classification, T. Miyato et al., ICLR 2017

Others

• Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, A. Nguyen et al., CVPR 2015

Talks

• Do Statistical Models Understand the World?, I. Goodfellow, 2015
• Classifiers under Attack, David Evans, 2017
• Adversarial Examples in Machine Learning, Nicolas Papernot, 2017
• Poisoning Behavioral Malware Clustering, Biggio. B, Rieck. K, Ariu. D, Wressnegger. C, Corona. I. Giacinto, G. Roli. F, 2014
• Is Data Clustering in Adversarial Settings Secure?, BBiggio. B, Pillai. I, Rota Bulò. S, Ariu. D, Pelillo. M, Roli. F, 2015
• Poisoning complete-linkage hierarchical clustering, Biggio. B, Rota Bulò. S, Pillai. I, Mura. M, Zemene Mequanint. E, Pelillo. M, Roli. F, 2014
• Is Feature Selection Secure against Training Data Poisoning?, Xiao. H, Biggio. B, Brown. G, Fumera. G, Eckert. C, Roli. F, 2015
• Adversarial Feature Selection Against Evasion Attacks, Zhang. F, Chan. PPK, Biggio. B, Yeung. DS, Roli. F, 2016

Licenses

License

To the extent possible under law, Yen-Chen Lin has waived all copyright and related or neighboring rights to this work.